Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.25765 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866918411420827648 |
|---|---|
| author | Chen, Binwei Leng, Huachao Mang, Chi Yeung Cheung, Tsz Wai Chen, Yanhua Loh, Wai Keung Anthony Wong, Chi Ho Tang, Chak Yin |
| author_facet | Chen, Binwei Leng, Huachao Mang, Chi Yeung Cheung, Tsz Wai Chen, Yanhua Loh, Wai Keung Anthony Wong, Chi Ho Tang, Chak Yin |
| contents | Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performance. However, deploying artificial intelligence (AI) for surface roughness classification is often constrained by the need for large labeled datasets and costly high-resolution imaging equipment. In this study, we explore the use of synthetic images, generated with Stable Diffusion XL, as an efficient alternative or supplement to experimentally acquired data for classifying ceramic surface roughness. We show that augmenting authentic datasets with generative images yields test accuracies comparable to those obtained using exclusively experimental images, demonstrating that synthetic images effectively reproduce the structural features necessary for classification. We further assess method robustness by systematically varying key training hyperparameters (epoch count, batch size, and learning rate), and identify configurations that preserve performance while reducing data requirements. Our results indicate that generative AI can substantially improve data efficiency and reliability in materials-image classification workflows, offering a practical route to lower experimental cost, accelerate model development, and expand AI applicability in materials engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25765 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Synthetic Images as Effective Substitutes for Experimental Data in Surface Roughness Classification Chen, Binwei Leng, Huachao Mang, Chi Yeung Cheung, Tsz Wai Chen, Yanhua Loh, Wai Keung Anthony Wong, Chi Ho Tang, Chak Yin Computer Vision and Pattern Recognition Materials Science Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performance. However, deploying artificial intelligence (AI) for surface roughness classification is often constrained by the need for large labeled datasets and costly high-resolution imaging equipment. In this study, we explore the use of synthetic images, generated with Stable Diffusion XL, as an efficient alternative or supplement to experimentally acquired data for classifying ceramic surface roughness. We show that augmenting authentic datasets with generative images yields test accuracies comparable to those obtained using exclusively experimental images, demonstrating that synthetic images effectively reproduce the structural features necessary for classification. We further assess method robustness by systematically varying key training hyperparameters (epoch count, batch size, and learning rate), and identify configurations that preserve performance while reducing data requirements. Our results indicate that generative AI can substantially improve data efficiency and reliability in materials-image classification workflows, offering a practical route to lower experimental cost, accelerate model development, and expand AI applicability in materials engineering. |
| title | Evaluating Synthetic Images as Effective Substitutes for Experimental Data in Surface Roughness Classification |
| topic | Computer Vision and Pattern Recognition Materials Science |
| url | https://arxiv.org/abs/2603.25765 |